Recent Arctic Sea Ice Variability: Connections to the Arctic Oscillation and the ENSO

2004 ◽  
Vol 31 (9) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jiping Liu ◽  
Judith A. Curry ◽  
Yongyun Hu
Keyword(s):  
Sea Ice ◽  
Arctic Oscillation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
The Arctic Oscillation

scholarly journals Implications of all season Arctic sea-ice anomalies on the stratosphere

2012 ◽  
Vol 12 (24) ◽  
pp. 11819-11831 ◽  
Author(s):  
D. Cai ◽  
M. Dameris ◽  
H. Garny ◽  
T. Runde
Keyword(s):  
Sea Ice ◽  
Boundary Conditions ◽  
Arctic Oscillation ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Time Slice ◽  
Wave Radiation ◽  
Arctic Sea ◽  
The Arctic Oscillation

Abstract. In this study the impact of a substantially reduced Arctic sea-ice cover on the lower and middle stratosphere is investigated. For this purpose two simulations with fixed boundary conditions (the so-called time-slice mode) were performed with a Chemistry-Climate Model. A reference time-slice with boundary conditions representing the year 2000 is compared to a second sensitivity simulation in which the boundary conditions are identical apart from the polar sea-ice cover, which is set to represent the years 2089–2099. Three features of Arctic air temperature response have been identified which are discussed in detail. Firstly, tropospheric mean polar temperatures increase up to 7 K during winter. This warming is primarily driven by changes in outgoing long-wave radiation. The tropospheric response (e.g. geopotential height anomaly) is in reasonable agreement with similar studies dealing with Arctic sea-ice decrease and the consequences on the troposphere. Secondly, temperatures decrease significantly in the summer stratosphere caused by a decline in outgoing short-wave radiation, accompanied by a slight increase of ozone mixing ratios. Thirdly, there are short periods of statistical significant temperature anomalies in the winter stratosphere probably driven by modified planetary wave activity, but generally there is no clear stratospheric response. The Arctic Oscillation (AO)-index, which is related to the troposphere–stratosphere coupling favours a more neutral state during winter. The only clear stratospheric response can be shown during November. Significant changes in Arctic temperature, meridional eddy heat fluxes and the Arctic Oscillation (AO)-index are detected. In this study the overall stratospheric response to the prescribed sea-ice anomaly is small compared to the tropospheric changes.

scholarly journals Dynamical Link between the Barents–Kara Sea Ice and the Arctic Oscillation

2016 ◽  
Vol 29 (14) ◽  
pp. 5103-5122 ◽  
Author(s):  
Xiao-Yi Yang ◽  
Xiaojun Yuan ◽  
Mingfang Ting
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Arctic Oscillation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Mean Flow ◽  
Stratospheric Polar Vortex ◽  
Arctic Sea ◽  
Barents And Kara Seas ◽  
The Arctic Oscillation

Abstract The recent accelerated Arctic sea ice decline has been proposed as a possible forcing factor for midlatitude circulation changes, which can be projected onto the Arctic Oscillation (AO) and/or North Atlantic Oscillation (NAO) mode. However, the timing and physical mechanisms linking AO responses to the Arctic sea ice forcing are not entirely understood. In this study, the authors suggest a connection between November sea ice extent in the Barents and Kara Seas and the following winter’s atmospheric circulation in terms of the fast sea ice retreat and the subsequent modification of local air–sea heat fluxes. In particular, the dynamical processes that link November sea ice in the Barents and Kara Seas with the development of AO anomalies in February is explored. In response to the lower-tropospheric warming associated with the initial thermal effect of the sea ice loss, the large-scale atmospheric circulation goes through a series of dynamical adjustment processes: The decelerated zonal-mean zonal wind anomalies propagate gradually from the subarctic to midlatitudes in about one month. The equivalent barotropic AO dipole pattern develops in January because of wave–mean flow interaction and firmly establishes itself in February following the weakening and warming of the stratospheric polar vortex. This connection between sea ice loss and the AO mode is robust on time scales ranging from interannual to decadal. Therefore, the recent winter AO weakening and the corresponding midlatitude climate change may be partly associated with the early winter sea ice loss in the Barents and Kara Seas.

scholarly journals The Effect of the Arctic Oscillation on the Predictability of Mid-High Latitude Circulation in December

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhihai Zheng ◽  
Jin Ban ◽  
Yongsheng Li
Keyword(s):  
Sea Ice ◽  
High Latitude ◽  
Arctic Oscillation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Latitudes ◽  
Arctic Sea ◽  
High Predictability ◽  
The Arctic Oscillation ◽  
The Impact

The impact of the Arctic Oscillation (AO) on the predictability of mid-high latitude circulation in December is analysed using a full set of hindcasts generated form the Beijing Climate Center Atmospheric General Circulation Model version 2.2 (BCC_AGCM2.2). The results showed that there is a relationship between the predictability of the model on the Eurasian mid-high latitude circulation and the phase of AO, with the highest predictability in the negative AO phase and the lowest predictability in the normal AO phase. Moreover, the difference of predictability exists at different lead times. The potential sources of the high predictability in the negative AO phase in the BCC_AGCM2.2 model were further diagnosed. It was found that the differences of predictability on the Eurasian mid-high latitude circulation also exist in different Arctic sea ice anomalies, and the model performs well in reproducing the response of Arctic sea ice on the AO. The predictability is higher when sudden stratospheric warming (SSW) events occur, and strong SSW events tend to form a negative AO phase distribution in the Eurasian mid-high latitudes both in the observation and model. In addition, the model captured the blocking over the mid-high latitudes well, it may be related to the relatively long duration of the blocking. Changes in the AO will affect the blocking circulations over the mid-high latitudes, which partly explains the high predictability of the model in negative AO phases from the aspect of the internal atmospheric dynamics.

1,500-year cycle in the Arctic Oscillation identified in Holocene Arctic sea-ice drift

2012 ◽  
Vol 5 (12) ◽  
pp. 897-900 ◽  
Author(s):  
Dennis A. Darby ◽  
Joseph D. Ortiz ◽  
Chester E. Grosch ◽  
Steven P. Lund
Keyword(s):  
Sea Ice ◽  
Arctic Oscillation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift ◽  
The Arctic Oscillation

scholarly journals A look at the date of snowmelt and correlations with the Arctic Oscillation

2013 ◽  
Vol 54 (62) ◽  
pp. 196-204 ◽  
Author(s):  
J.L. Foster ◽  
J. Cohen ◽  
D.A. Robinson ◽  
T.W. Estilow
Keyword(s):  
Snow Cover ◽  
Arctic Oscillation ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
The Arctic ◽  
Phytoplankton Abundance ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
The Arctic Oscillation ◽  
Arctic Sea Ice Extent

AbstractSpring snow cover across Arctic lands has, on average, retreated ∼5 days earlier since the late 1980s compared to the previous 20 years. However, it appears that since about the late 1980s the date the snowline first retreats north during the spring has changed only slightly: in the last ∼20 years snow has not been disappearing significantly earlier. Snowmelt changes observed since the late 1980s have been step-like, unlike the more continuous downward trend seen in Arctic sea-ice extent. At 70° N, several longitudinal segments (of 10°) show significant (negative) trends, while only two longitudinal segments at 60° N show significant trends, one positive and one negative. These variations appear to be related to variations in the Arctic Oscillation (AO). When the springtime AO is strongly positive, snow melts earlier. When it is strongly negative, snow disappears later in the spring. The winter AO is less straightforward. At higher latitudes (70° N), a positive AO during the winter months is correlated with later snowmelt, but at lower latitudes (50° N and 60° N) a positive wintertime AO is correlated with earlier snowmelt. If the AO during the winter months is negative, the reverse is true. Similar stepwise changes (since the late 1980s) have been noted in sea surface temperatures and in phytoplankton abundance as well as in snow cover.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals Polar cod in jeopardy under the retreating Arctic sea ice

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mats Brockstedt Olsen Huserbråten ◽  
Elena Eriksen ◽  
Harald Gjøsæter ◽  
Frode Vikebø
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Keystone Species ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Biophysical Model ◽  
The Arctic ◽  
Polar Cod ◽  
Arctic Sea ◽  
Shelf Sea

Abstract The Arctic amplification of global warming is causing the Arctic-Atlantic ice edge to retreat at unprecedented rates. Here we show how variability and change in sea ice cover in the Barents Sea, the largest shelf sea of the Arctic, affect the population dynamics of a keystone species of the ice-associated food web, the polar cod (Boreogadus saida). The data-driven biophysical model of polar cod early life stages assembled here predicts a strong mechanistic link between survival and variation in ice cover and temperature, suggesting imminent recruitment collapse should the observed ice-reduction and heating continue. Backtracking of drifting eggs and larvae from observations also demonstrates a northward retreat of one of two clearly defined spawning assemblages, possibly in response to warming. With annual to decadal ice-predictions under development the mechanistic physical-biological links presented here represent a powerful tool for making long-term predictions for the propagation of polar cod stocks.

scholarly journals On the Link between Arctic Sea Ice Decline and the Freshwater Content of the Beaufort Gyre: Insights from a Simple Process Model

2014 ◽  
Vol 27 (21) ◽  
pp. 8170-8184 ◽  
Author(s):  
Peter E. D. Davis ◽  
Camille Lique ◽  
Helen L. Johnson
Keyword(s):  
Sea Ice ◽  
Momentum Transfer ◽  
Momentum Flux ◽  
Process Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Simple Process ◽  
Beaufort Gyre ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract Recent satellite and hydrographic observations have shown that the rate of freshwater accumulation in the Beaufort Gyre of the Arctic Ocean has accelerated over the past decade. This acceleration has coincided with the dramatic decline observed in Arctic sea ice cover, which is expected to modify the efficiency of momentum transfer into the upper ocean. Here, a simple process model is used to investigate the dynamical response of the Beaufort Gyre to the changing efficiency of momentum transfer, and its link with the enhanced accumulation of freshwater. A linear relationship is found between the annual mean momentum flux and the amount of freshwater accumulated in the Beaufort Gyre. In the model, both the response time scale and the total quantity of freshwater accumulated are determined by a balance between Ekman pumping and an eddy-induced volume flux toward the boundary, highlighting the importance of eddies in the adjustment of the Arctic Ocean to a change in forcing. When the seasonal cycle in the efficiency of momentum transfer is modified (but the annual mean momentum flux is held constant), it has no effect on the accumulation of freshwater, although it does impact the timing and amplitude of the annual cycle in Beaufort Gyre freshwater content. This suggests that the decline in Arctic sea ice cover may have an impact on the magnitude and seasonality of the freshwater export into the North Atlantic.

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